Introduction

FlinkML is designed to make learning from your data a straight-forward process, abstracting away
the complexities that usually come with big data learning tasks. In this
quick-start guide we will show just how easy it is to solve a simple supervised learning problem
using FlinkML. But first some basics, feel free to skip the next few lines if you’re already
familiar with Machine Learning (ML).

As defined by Murphy [1] ML deals with detecting patterns in data, and using those
learned patterns to make predictions about the future. We can categorize most ML algorithms into
two major categories: Supervised and Unsupervised Learning.

Supervised Learning deals with learning a function (mapping) from a set of inputs
(features) to a set of outputs. The learning is done using a training set of (input,
output) pairs that we use to approximate the mapping function. Supervised learning problems are
further divided into classification and regression problems. In classification problems we try to
predict the class that an example belongs to, for example whether a user is going to click on
an ad or not. Regression problems one the other hand, are about predicting (real) numerical
values, often called the dependent variable, for example what the temperature will be tomorrow.

Unsupervised Learning deals with discovering patterns and regularities in the data. An example
of this would be clustering, where we try to discover groupings of the data from the
descriptive features. Unsupervised learning can also be used for feature selection, for example
through principal components analysis.

Linking with FlinkML

In order to use FlinkML in your project, first you have to
set up a Flink program.
Next, you have to add the FlinkML dependency to the pom.xml of your project:

Loading data

To load data to be used with FlinkML we can use the ETL capabilities of Flink, or specialized
functions for formatted data, such as the LibSVM format. For supervised learning problems it is
common to use the LabeledVector class to represent the (label, features) examples. A LabeledVector
object will have a FlinkML Vector member representing the features of the example and a Double
member which represents the label, which could be the class in a classification problem, or the dependent
variable for a regression problem.

As an example, we can use Haberman’s Survival Data Set , which you can
download from the UCI ML repository.
This dataset “contains cases from a study conducted on the survival of patients who had undergone
surgery for breast cancer”. The data comes in a comma-separated file, where the first 3 columns
are the features and last column is the class, and the 4th column indicates whether the patient
survived 5 years or longer (label 1), or died within 5 years (label 2). You can check the UCI
page for more information on the data.

We can now transform the data into a DataSet[LabeledVector]. This will allow us to use the
dataset with the FlinkML classification algorithms. We know that the 4th element of the dataset
is the class label, and the rest are features, so we can build LabeledVector elements like this:

We can then use this data to train a learner. We will however use another dataset to exemplify
building a learner; that will allow us to show how we can import other dataset formats.

LibSVM files

A common format for ML datasets is the LibSVM format and a number of datasets using that format can be
found in the LibSVM datasets website. FlinkML provides utilities for loading
datasets using the LibSVM format through the readLibSVM function available through the MLUtils
object.
You can also save datasets in the LibSVM format using the writeLibSVM function.
Let’s import the svmguide1 dataset. You can download the
training set here
and the test set here.
This is an astroparticle binary classification dataset, used by Hsu et al. [3] in their
practical Support Vector Machine (SVM) guide. It contains 4 numerical features, and the class label.

This gives us two DataSet objects that we will use in the following section to
create a classifier.

Classification

Once we have imported the dataset we can train a Predictor such as a linear SVM classifier.
We can set a number of parameters for the classifier. Here we set the Blocks parameter,
which is used to split the input by the underlying CoCoA algorithm [2] uses. The
regularization parameter determines the amount of $l_2$ regularization applied, which is used
to avoid overfitting. The step size determines the contribution of the weight vector updates to
the next weight vector value. This parameter sets the initial step size.

We can now make predictions on the test set, and use the evaluate function to create (truth, prediction) pairs.

valevaluationPairs:DataSet[(Double, Double)]=svm.evaluate(astroTest)

Next we will see how we can pre-process our data, and use the ML pipelines capabilities of FlinkML.

Data pre-processing and pipelines

A pre-processing step that is often encouraged [3] when using SVM classification is scaling
the input features to the [0, 1] range, in order to avoid features with extreme values
dominating the rest.
FlinkML has a number of Transformers such as MinMaxScaler that are used to pre-process data,
and a key feature is the ability to chain Transformers and Predictors together. This allows
us to run the same pipeline of transformations and make predictions on the train and test data in
a straight-forward and type-safe manner. You can read more on the pipeline system of FlinkML
in the pipelines documentation.

Let us first create a normalizing transformer for the features in our dataset, and chain it to a
new SVM classifier.

We can now use our newly created pipeline to make predictions on the test set.
First we call fit again, to train the scaler and the SVM classifier.
The data of the test set will then be automatically scaled before being passed on to the SVM to
make predictions.

Where to go from here

This quickstart guide can act as an introduction to the basic concepts of FlinkML, but there’s a lot
more you can do.
We recommend going through the FlinkML documentation, and trying out the different
algorithms.
A very good way to get started is to play around with interesting datasets from the UCI ML
repository and the LibSVM datasets.
Tackling an interesting problem from a website like Kaggle or
DrivenData is also a great way to learn by competing with other
data scientists.
If you would like to contribute some new algorithms take a look at our
contribution guide.